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README.md

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+## 🚀 Qwen2.5 1.5B Python Coder  
+**Supervised Fine-Tuning (SFT) + VERL Reinforcement Learning**
+
+---
+
+### 🧠 Training Overview
+
+#### 🔹 Supervised Fine-Tuning (SFT)
+- **Hardware**: 2× T4 GPUs (Kaggle)
+- **Dataset**: https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca
+
+#### 🔹 Reinforcement Learning (VERL)
+- **Platform**: L4 GPU (Google Colab)
+- **Samples**: 2,000  
+- **Dataset**: https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-4o  
+- **Reward Function**:  
+  - Based on the **proportion of unit tests passed**
+
+---
+
+### 📊 Evaluation
+
+- **Benchmark**: https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
+
+| Model Variant        | Score |
+|---------------------|-------|
+| Baseline (Plain)    | 0.000 |
+| After SFT           | 0.165 |
+| After SFT + VERL    | 0.287 |
+
+---
+
+### ✨ Summary
+
+- SFT provides a strong initial boost in coding capability  
+- VERL further improves performance by reinforcing test-passing behavior  
+- Combined approach yields a **~74% improvement over SFT alone**